Methane (CH4) emissions from climate-sensitive ecosystems within the northern permafrost region represent a large but highly uncertain source, with current estimates spanning a factor of seven (11 – 75 Tg CH4 yr-1). Accelerating permafrost thaw threatens significant increases in pan-Arctic CH4 emissions, amplifying the permafrost carbon feedback. We used airborne imaging spectroscopy with meter-scale spatial resolution and broad coverage to identify a previously undiscovered CH4 hotspot adjacent to an intensively studied thermokarst lake in interior Alaska. Hotspot emissions were confined to < 1% of the 10 ha study area. Ground-based chamber measurements confirmed average daily fluxes of 1,170 mg CH4 m-2 d-1, with extreme daily maxima up to 24,200 mg CH4 m-2 d-1. Ground-based geophysics measurements revealed thawed permafrost at and directly beneath the CH4 hotspot, extending to a depth of ~15 m, indicating that the intense CH4 emissions likely originated from recently thawed permafrost. Emissions from the hotspot accounted for ~40% of total diffusive CH4 emissions from the entire study area. Combining these results with hotspot statistics from our 70,000 km2 airborne survey across Alaska and northwestern Canada, we estimate that terrestrial thermokarst hotspots currently emit 1.1 (0.1 – 5.2) Tg CH4 yr-1, or roughly 4% of the annual pan-Arctic wetland budget from just 0.01% of the northern permafrost land area. Our results suggest that significant proportions of pan-Arctic CH4 emissions originate from disproportionately small areas of previously undetermined thermokarst emissions hotspots, and that pan-Arctic CH4 emissions may increase non-linearly as thermokarst processes increase under a warming climate.

Imaging spectroscopy is a remote-sensing technique that retrieves reflectances across visible to shortwave infrared wavelengths at high spectral resolution (<10 nm). Spectroscopic reflectance data provide novel information on the properties of the Earth’s terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were limited spatially and temporally using airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG), providing the main source of observations. Here, we present a land-surface modeling framework to help support end-to-end traceability of emerging imaging spectroscopy spaceborne missions. The LPJ-wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra. LPJ-wsl variables are cross-walked to meet required PROSAIL parameters, which include leaf structure, Chlorophyll a+b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, and a tropical forest site using reflectance data from canopy imagers mounted on towers and from air and spaceborne platforms. We find that canopy nitrogen and leaf-area index are the most uncertain variables in translating LPJ-wsl to PROSAIL parameters but at first order, LPJ-PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ-model to represent improvements in leaf water content and canopy nitrogen. The LPJ-PROSAIL model can support missions such as NASA’s Surface Biology and Geology (SBG) and higher-level modeled products.

Snow and ice melt processes are a key in Earth’s energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth’s ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/g_ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.

High-resolution space-based spectral imaging of the Earth’s surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise. The different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA’s Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the intrinsic dimensionality decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. Intrinsic dimensionality is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.